递归地调用"WindowSummarizer“(从sktime开始)到未来的预测目标Y

Question

我需要关于如何在将来递归地使用SKTIME库中的WindowSummarizer函数的帮助。

因为在训练和测试数据中，很容易产生移动平均值和移动标准差的变量。

但是对于未来的预测，WindowSummarizer函数必须以交互的方式工作，对于每一个新的预测观测，WindowSummarizer必须重新计算所有变量，从而递归地进行下一个预测，直到最后。

有谁可以帮我？

下面是一个可重复的例子：

#!pip install sktime[all_extras]

import pandas as pd
import numpy as np
from sktime.transformations.series.summarize import WindowSummarizer
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.compose import ForecastingPipeline
from sklearn.linear_model import LinearRegression
from sktime.datasets import load_airline
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.compose import make_reduction, TransformedTargetForecaster

y = load_airline()

kwargs = {
        "lag_feature": {
            "lag": [0,1],
            "mean": [[0, 7], [0, 14],[0, 28]],
            "std": [[0, 7], [0, 14],[0, 28]],
            "kurt": [[0, 7], [0, 14],[0, 28]],
            "skew": [[0, 7], [0, 14],[0, 28]]
        }
    }


forecaster = make_reduction(
    LinearRegression(),
    scitype="tabular-regressor",
    transformers=[WindowSummarizer(**kwargs, n_jobs=1,truncate = "bfill")],
    window_length=None,
    strategy="recursive"
)

pipe = ForecastingPipeline(
    steps=[
        ("forecaster", forecaster),
    ]
)

model = pipe.fit(y)

model.predict(fh = np.arange(1,100,1))

此代码返回错误消息：

Input contains NaN, infinity or a value too large for dtype('float64').

Answer 1

这里的问题是您使用的是ForecastingPipeline而不是TransformedTargetForecaster。根据文件.“利用外源data.ForecastingPipeline进行预测的管道只将给定的变压器应用到X上，预测者也可以是包含变压器的TransformedTargetForecaster来转换y”。

因此，对TransformedTargetForecaster的一个简单的更改适用于您发布的代码..。

y = load_airline()

kwargs = {
        "lag_feature": {
            "lag": [0,1],
            "mean": [[0, 7], [0, 14],[0, 28]],
            "std": [[0, 7], [0, 14],[0, 28]],
            "kurt": [[0, 7], [0, 14],[0, 28]],
            "skew": [[0, 7], [0, 14],[0, 28]]
        }
    }
from sklearn.linear_model import LinearRegression

forecaster2 = make_reduction(
    LinearRegression(),
    scitype="tabular-regressor",
    transformers=[WindowSummarizer(**kwargs, n_jobs=1,truncate = "bfill")],
    window_length=None,
    strategy="recursive"
)

pipe = TransformedTargetForecaster(
    steps=[
        ("forecaster", forecaster2),
    ]
)

model = pipe.fit(y)

model.predict(fh = np.arange(1,100,1))

您还可以使用以下代码检查WindowSummarizer创建的外生变量.

transformer = WindowSummarizer(**kwargs, n_jobs=1,truncate = "bfill")
y_transformed = transformer.fit_transform(y)

希望这能有所帮助。